Xiaoyong Shen

11.1k citations
43 papers · 3.9k indexed · 3 hit papers · h-index 26

Xiaoyong Shen

43 papers receiving 3.9k citations

Hit Papers

Underexposed Photo Enhancement Using Deep Illumination Es...2019202620212023201920192019200400600

Peers

Xiaoyong Shen
Comparison fields: 5 of 116
  • Computer Vision and Pattern Recognition 3.5k
  • Media Technology 765
  • Aerospace Engineering 640
  • Artificial Intelligence 510
  • Computational Mechanics 415
Replace Yuchao Dai with:
Yuchao Dai China
Philipp Krähenbühl United States
Bharath Hariharan United States
René Ranftl Switzerland
Ioannis Pratikakis Greece
Shuaicheng Liu China
Hassan Foroosh United States
Qingxiong Yang Hong Kong
Yongming Rao China
Kari Pulli United States
Xiaoyong Shen relative to Yuchao Dai China Yuchao Dai's profile →
Citations per field
00.5×1.5×
Yuchao Dai · 1×
Citations per year

Countries citing papers authored by Xiaoyong Shen

Since Specialization
Citations

This map shows the geographic impact of Xiaoyong Shen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xiaoyong Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoyong Shen more than expected).

Fields of papers citing papers by Xiaoyong Shen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiaoyong Shen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xiaoyong Shen. The network helps show where Xiaoyong Shen may publish in the future.

Co-authorship network of co-authors of Xiaoyong Shen

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaoyong Shen. A scholar is included among the top collaborators of Xiaoyong Shen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xiaoyong Shen. Xiaoyong Shen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 8
2 4
3 2
4
STD: Sparse-to-Dense 3D Object Detector for Point Cloudbreakdown →
569
5 86
6
Fast Point R-CNNbreakdown →
266
7 163
8 28
9 142
10 154
11
Image inpainting via generative multi-column convolutional neural networks
114
12 1
13 9
14 100
15 14
16 249
17 39
18
Description and reasoning method of uncertain temporal knowledge based on IFTPN
3
19 6
20
The Semantic Match Degree for Intuitionistic Fuzzy Reasoning
1

About Xiaoyong Shen

Xiaoyong Shen is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Media Technology, having authored 43 papers that have together received 3.9k indexed citations. Recurring topics across this work include Advanced Vision and Imaging (12 papers), Advanced Image Processing Techniques (10 papers) and Image Enhancement Techniques (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (3.5k citations), Media Technology (765 citations) and Geology (277 citations). Xiaoyong Shen has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Jiaya Jia, Shu Liu, Chi‐Wing Fu, Yanan Sun, Zetong Yang, Xin Tao, Wei‐Shi Zheng, Ruixing Wang, Qing Zhang and Yilun Chen. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Transactions on Graphics and International Journal of Computer Vision.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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